Citation

Xian S, Qi Z, Yip N. J. Transp. Geogr. 2022; 99: e103304.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.jtrangeo.2022.103304

PMID

unavailable

Abstract

Recent studies on socio-spatial segregation have revealed the uneven segregation experiences of individuals within their daily life contexts. However, little is known about its temporal variations across the week. The advancement of GIS and GPS tracking technology also poses methodological challenges in processing rich mobility-activity data efficiently in identifying the socio-spatial segregation patterns. With data collected by a mobile phone app that ran on the participants' mobile phone for a whole week, this paper integrates the spatial, temporal, mobility, and activity dimensions with the demographic data to segregation patterns of the participants and assesses segregation at the individual level. Our findings indicate that the socio-spatial segregation level decreased in the daytime and increased at night, and this pattern was consistent across a week. However, no significant differences are found between different age groups, occupation, housing types and home neighborhood types. To improve the efficiency of data processing, this paper employs decision tree algorithms supplemented by the analysis of variance and Tukey's honestly significant difference test to identify meaningful mobility-activity patterns with significant intergroup differences. It is able to pinpoint temporal and spatial activity-mobility patterns that crosscut home location, location of workplace, and socioeconomic status. It also helps connect residential segregation and segregation that goes beyond the home neighborhoods.

Language: en

Keywords

Activity; Decision tree algorithms; Housing tenure status; Mobility; Socio-spatial segregation